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Training Neural Networks for Reading Handwritten Amounts on Checks

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Author Info

  • Gupta, Amar
  • Palacios, Rafael

Abstract

While reading handwritten text accurately is a difficult task for computers, the conversion of handwritten papers into digital format is necessary for automatic processing. Since most bank checks are handwritten, the number of checks is very high, and manual processing involves significant expenses, many banks are interested in systems that can read check automatically. This paper presents several approaches to improve the accuracy of neural networks used to read unconstrained numerals in the courtesy amount field of bank checks.

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File URL: http://hdl.handle.net/1721.1/699
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Bibliographic Info

Paper provided by Massachusetts Institute of Technology (MIT), Sloan School of Management in its series Working papers with number 4365-02.

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Date of creation: 07 Jun 2002
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Handle: RePEc:mit:sloanp:699

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Postal: MASSACHUSETTS INSTITUTE OF TECHNOLOGY (MIT), SLOAN SCHOOL OF MANAGEMENT, 50 MEMORIAL DRIVE CAMBRIDGE MASSACHUSETTS 02142 USA
Phone: 617-253-2659
Web page: http://mitsloan.mit.edu/
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Postal: MASSACHUSETTS INSTITUTE OF TECHNOLOGY (MIT), SLOAN SCHOOL OF MANAGEMENT, 50 MEMORIAL DRIVE CAMBRIDGE MASSACHUSETTS 02142 USA

Related research

Keywords: Optical Character Recognition; Unconstrained Handwritten Numerals; Check Processing; Document Imaging; Neural Networks;

References

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  1. Joanna Stavins, 1997. "A comparison of social costs and benefits of paper check presentment and ECP with truncation," New England Economic Review, Federal Reserve Bank of Boston, issue Jul, pages 27-44.
  2. Kirstin E. Wells, 1996. "Are checks overused?," Quarterly Review, Federal Reserve Bank of Minneapolis, issue Fall, pages 2-12.
  3. Palacios, Rafael & Wang, Patrick S.P. & Gupta, Amar, 2002. "Reading Courtesy Amounts on Handwritten Paper Checks," Working papers 4364-02, Massachusetts Institute of Technology (MIT), Sloan School of Management.
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